Validation of data preprocessing to guarantee high accuracy in deep learning treatments: Agricultural domain as a case of study

Abstract

The agricultural sector occupies a great importance to mankind, and it is constantly striving to strengthen and develop its systems. Similar to other fields, the agricultural industry has adopted deep learning techniques to process agricultural data, with the aim of achieving high quality crop results. In different regions as Europe, North America, and East Asia, these techniques have been used to identify plant diseases, their causes, and even predict crop yields in certain seasons. However, we have noticed a lack of interest in applying these technologies in the desert environment, because it is completely different in terms of non-fertile soil quality, drought, water salinity, extreme temperatures, and so on. Given the above conditions, we propose to use previously developed models based on convolutional neural networks to solve the aforementioned problems. Our focus is to identify and classify tomato leaf diseases using a specially collected dataset from a desert environment. KeywordsP@Q « úΫ . é JÒ ¢

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